Toward Monitoring Parkinson's Through Analysis of Static Handwriting Samples: A Quantitative Analytical Framework

A Smartphone Application for Automated Decision Support in Cognitive Task Based Evaluation of Central Nervous System Motor Disorders

BACKGROUND AND OBJECTIVE

New technology enables constant boost to the powers of mobile devices, which in the previous years have transformed from simple mobile phones to smart phones. Computational powers of these electronics enable actions that previously were possible only for computers. By the use of special applications, we may benefit from sensors and multimedia capabilities of operating systems. Therefore, a new era for devoted implementations opens, in which a smart application can take a role of computing system to estimate the symptoms of diseases by evaluating signals coming from a human body.

METHODS

We propose a model of an application implemented for mobile android systems, which can be used for examination of central nervous system motor disorders occurring in patients suffering from Huntington (HD), Alzheimer, or Parkinson diseases. In particular, the model tracks tremors (involuntary movements), and cognitive (memory loss or dementia) impairments using touch and visual stimulus modalities. The proposed model interprets the symptoms from human bodies that indicate one of the diseases of the nervous system. Pre-processing of collected data for feature extraction is executed on a mobile device by using core functionality and methods provided in android's application programming interface. The information is evaluated by a back-propagation neural network classifier and the result is presented to the end user. The system is able to contact medical supervision and provide an assistance from the clinic.

RESULTS

The system uses a collected dataset of 1928 records, taken from 11 HD patients and 11 healthy persons in Lithuania, to gather statistics about examinations and presents the results as medical evaluation with prediction on the state of health. The accuracy of recognition of early, prodromal symptoms for central nervous system motor disorders is 86.4% (F-measure 0.859). The app (available on Google Play) is easy to use and is efficient tool for decision support in medical examinations.

CONCLUSIONS

The use of intelligent apps which can help to evaluate neurodegenerative disorders is an important enhancement to medical diagnosis. The developed smartphone app supports the doctor with additional results that are easy to compare with other examinations. This kind of examination is a nice change from classic stereotypes, especially for younger age patients, who are used to various aspects of information technology.

A New Approach to Diagnose Parkinson's Disease Using a Structural Cooccurrence Matrix for a Similarity Analysis

Parkinson's disease affects millions of people around the world and consequently various approaches have emerged to help diagnose this disease, among which we can highlight handwriting exams. Extracting features from handwriting exams is an important contribution of the computational field for the diagnosis of this disease. In this paper, we propose an approach that measures the similarity between the exam template and the handwritten trace of the patient following the exam template. This similarity was measured using the Structural Cooccurrence Matrix to calculate how close the handwritten trace of the patient is to the exam template. The proposed approach was evaluated using various exam templates and the handwritten traces of the patient. Each of these variations was used together with the Naïve Bayes, OPF, and SVM classifiers. In conclusion the proposed approach was proven to be better than the existing methods found in the literature and is therefore a promising tool for the diagnosis of Parkinson's disease.

Validity and reliability of a new tool to evaluate handwriting difficulties in Parkinson's disease

BACKGROUND

Handwriting in Parkinson's disease (PD) features specific abnormalities which are difficult to assess in clinical practice since no specific tool for evaluation of spontaneous movement is currently available.

OBJECTIVE

This study aims to validate the 'Systematic Screening of Handwriting Difficulties' (SOS-test) in patients with PD.

METHODS

Handwriting performance of 87 patients and 26 healthy age-matched controls was examined using the SOS-test. Sixty-seven patients were tested a second time within a period of one month. Participants were asked to copy as much as possible of a text within 5 minutes with the instruction to write as neatly and quickly as in daily life. Writing speed (letters in 5 minutes), size (mm) and quality of handwriting were compared. Correlation analysis was performed between SOS outcomes and other fine motor skill measurements and disease characteristics. Intrarater, interrater and test-retest reliability were assessed using the intraclass correlation coefficient (ICC) and Spearman correlation coefficient.

RESULTS

Patients with PD had a smaller (p = 0.043) and slower (p<0.001) handwriting and showed worse writing quality (p = 0.031) compared to controls. The outcomes of the SOS-test significantly correlated with fine motor skill performance and disease duration and severity. Furthermore, the test showed excellent intrarater, interrater and test-retest reliability (ICC > 0.769 for both groups).

CONCLUSION

The SOS-test is a short and effective tool to detect handwriting problems in PD with excellent reliability. It can therefore be recommended as a clinical instrument for standardized screening of handwriting deficits in PD.